F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen,

F. J. SoaresINESC Porto/FEUP

Smart charging strategies for efficient

management of the grid and

generation systems

Electric Vehicle Integration Into Modern Power Networks

24 September 2010

DTU, Copenhagen

Summary

1. The Electric Mobility Paradigm

a) Motives for EV adoption

b) Expectable benefits

c) Foreseen problems for electric power systems

d) Predicted EV rollout in some EU countries

2. Conceptual Framework for EV Integration Into Electric Power Systems

a) The EV supplier/aggregator

b) Possible EV charging approaches

3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies

a) Case study A: typical Portuguese LV grid

b) Case study B: typical Portuguese MV grid

c) Overall conclusions

4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method

a) Introduction

b) Case study: Flores Island network (Azores Archipelago)

c) EV motion simulation

d) Monte Carlo Algorithm

e) Results

f) Conclusions

5. Final Remarks



Extremely volatile oil prices with a rising trend (due to increasing demand)

So

urc

e:

oil-

price.n

et



High concentration of GHG in the atmosphere (global problem)

So

urc

e:

wik

ipedia

.org

So

urc

e:

wik

ipedia

.org



High pollution levels in areas with high population density (local problem)

Source: SMH

Source: isiria.wordpress.com

Source: fearsmag.com



Reduction of the fossil fuel usage in the transportations sector

Immediate reduction of the local pollution levels

(CO2, CO, HC, NOX, PM)

If EV deployment is properly accompanied by an increase in

the exploitation of renewable endogenous resources

GHG global emissions will be greatly reduced Important

contribution to eradicate the global warming problematic

Source: topnews.in

So

urc

e:

myclim

ate

ch

ang

e.n

et



EV capability to inject power into the grid (V2G concept) might be used to

“shape” the power demand, avoiding very high peak loads and energy losses

EV storage capability might be used to avoid wasting “clean” energy

(wind/PV) in systems with a high share of renewables

During the periods when renewable power available

is higher than the consumption

Isolated networks might improve their robustness and safely accommodate a

larger quantity of intermittent renewable energy sources

If EV batteries are efficiently exploited as storage devices

and used to mitigate frequency oscillations


c) Foreseen problems for electric power systems

Depending on the number of EV present in the grid, the increase in the

power demand will lead to:

• Branches overloading

• Under voltage problems

• Significant increase of the energy losses

• Substation transformers overloading

• Need to invest in new generation facilities to face increasing demand

• Aggravation of the voltage imbalances between phases (for single phase

EV/Grid connections)



Almost no official information available

Contradictory information from non official sources

Difficult to make accurate network

impact studies

Source: Ricardo plc 2010

Source: Ricardo plc 2010

ACEA - European Automobile Manufacturers' Association



Types of EV available:

Plug-in Hybrid EV use a small battery

and a generator combined with an ICE

Fuel Cell EV store energy in H2 which

feeds a fuel cell that produces electricity

and heat

Battery EV powered only by electricity,

which requires a large battery pack



Single EV do not have enough “size” to participate in electricity markets

If grouped through an aggregator agent, EV might sell several system services

in the markets

The EV suppliers/aggregators:

are completely independent from the DSO

act as an interface between EV and electricity markets

group EV, according to their owners’ willingness, to exploit business

opportunities in the electricity markets

develop their activities along a large geographical area (e.g. a country)



EV

supplier/aggregator

structure:Regional Aggregation Unit

Microgrid Aggregation Unit


CVC

CVC

CVC


MV Level

LV Level

Smart Meter

VC

Smart Meter

VC

Smart Meter

VC

Smart Meter

VC

Smart Meter

VC

Smart Meter

VC

EV Owner

EV Owner

EV Owner

EV Owner

EV Owner

EV Owner

SU

PP

LIE

R/A

GG

RE

GA

TO

R

Regional Aggregation Unit



CVC

CVC

CVC


MV Level

LV Level

Smart Meter

VC

Smart Meter

VC

Smart Meter

VC

Smart Meter

VC

Smart Meter

VC

Smart Meter

VC

EV Owner

EV Owner

EV Owner

EV Owner

EV Owner

EV Owner

• Regional

Aggregation Unit

(RAU) – located at

the HV/MV

substation level and

covering a region

(e.g. a large city) with

~20000 clients

• Microgrid

Aggregation Unit

(MGAU) – located at

the MV/LV substation

level and covering a

LV grid with ~400

clients

PLAYERSCONTROL HIERARCHY

DMS

CAMC

CVC

MGCC

Control

Level 3

VC

RAU

MGAU

TSO

GENCO

DSO

Control

Level 1

Control

Level 2

EV Supplier/AggregatorDis

trib

uti

on

Sy

ste

m

Transmission System

Generation System

Ele

ctr

icity M

ark

et

Op

era

tors

Technical Operation Market Operation

Electric Energy

Electric Energy

Technical Validation of the Market Negotiation (for the transmission system)

Electric Energy

Reserves

Reserves

Parking Parking Battery

Replacement

Battery

Replacement

EV

Owner/Electricity

Consumer

Parking

Facilities

Battery

Suppliers

Electricity

Consummer

Electricity

Consumer

Electric Energy

Controls (in normal system operation) At the level of

Communicates with

Sell offer

Buy offer

Technical validation of the market results

Controls (in abnormal system operation/emergency mode)

Reserves



DMS – Distribution Management System CAMC – Central Autonomous Management System MGCC – MicroGrid Central Controller

CVC – Cluster of Vehicles Controller VC – Vehicle Controller



EV as uncontrollable static loads:

EV owners define when and where EV will charge, how much power they will require

from the grid and the period during which they will be connected to it

EV as controllable dynamic loads:

EV owners give the aggregator the possibility to manage their charging during the

period they are connected to the grid

They only inform the aggregator about the time during which their vehicles will be

connected to the grid and the batteries’ SOC they desire at the end of that same period

EV as controllable dynamic loads and storage devices:

EV are not regarded just as dynamic loads but also as dispersed energy storage

devices

They can be used either to absorb energy and store it or inject electricity to grid,

acting in a V2G perspective



Charging approaches:

Charging Modes

Uncontrolled

Dumb Charging (DC)

Multiple Prices Tariff (MPT)

Controlled

Smart Charging (SC)

Vehicle-to-Grid (V2G)



Uncontrolled approaches:

Dumb charging EV owners are completely free to charge their vehicles whenever they want;

electricity price is assumed to be constant along the day

Multiple prices tariff EV owners are completely free to charge their vehicles whenever they

want; electricity price is assumed not to be constant along the day, existing some periods where its

cost is lower

EV

AMM

µG

µG

Storage

Energy absorbed and

charging period of a single EV

EV Charger

Charging starts when

EV is plugged-in

Billing and

tariffsInformation about interruptions

and disconnection orders in

case of grid problems

DSO Aggregator

Power

consumed

MarketResponsible for the

grid technical

operation



Controllable approaches:

Smart charging active management system where there is an aggregator serving as link

between the electricity market and EV owners; enables congestion prevention and voltage control

V2G mode of operation besides the charging, the aggregator controls the power that EV might

inject into the grid; EV have the capability to provide peak power and to perform frequency control

EV

AMM

µG

µG

Storage

Period during which a single EV will be

connected to the grid and the required

battery SOC at the end of that time

EV Charger

EV is plugged-in and its owner

defines the disconnection hour

and the required battery SOC

Broadcast of information related

with billing, tariffs, set-points to

adjust EV control parameters and

SC/V2G set-points in accordance

with the market negotiations

DSOAggregator

Information about interruptions

and disconnection orders in

case of grid problems

Power

consumed

MarketResponsible for the

grid technical

operation


a) Case study A: typical Portuguese MV grid

Objectives:

Quantify the maximum percentage of conventional vehicles that can be

replaced by EV, without compromising grid normal operation, using three

different charging approaches:

• Dumb charging

• Dual tariff policy (= multiple prices tariff)

• Smart charging

Compare grid behaviour when subjected to different percentages of EV

and when different charging approaches are implemented



Grid architecture:

Semi-urban MV network (15 kV)

Two feeding points voltage 1.05 p.u.

Consumption during a typical weekday

271.1 MWh

Peak load 16.6 MW

0

2

4

6

8

10

12

14

16

18

1 5 9 13 17 21

Co

nsu

mp

tio

n (M

W)

Hour

Total

Household

Commercial

Industrial



EV characterization and modelling:

Initially, 635 EV (~5%) were distributed through the grid proportionally to

the residential load installed at each bus

12700 vehicles

Annual mileage 12800 km (35 km/day)

EV assumed charging time 4h

EV fleet considered:

• Large EV 24 kWh 40% of the EV fleet

• Medium EV 12 kWh 40% of the EV fleet

• Plug-in Hybrid EV 6 kWh 20% of the EV fleet



Dumb charging and dual tariff policy methodology

Distribute EV through the grid proportionally to the residential power installed in each node

Define the initial share of conventional vehicles replaced by EV

Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (dumb charging mode)

Run a power flow for the current hour

Feasible operating conditions ?

Yes

No

Calculate, in a hourly basis, the total nodal load

End of day was reached ?

No

Yes

Maximum share of EV was reached

Define, in a hourly basis, the nodal conventional load (residential, commercial and industrial) of the grid

Next hour

Increase the share of EV in 1%

Algorithm developed

to quantify the

maximum number of

EV that can be safely

integrated into the

grid with the dumb

charging (without

grid reinforcements)

3. Evaluation of EV Impacts in

Distribution Networks –

Preliminary Studies

a) Case study A: typical Portuguese

MV grid

Smart charging methodology

Algorithm developed to

maximize the number of EV

that can be safely integrated

in the grid with the smart

charging (without grid

reinforcements)



Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (as in the dumb

charging mode)



Halt the charging

of 2% of the EV

connected in each

node downstream

the problematic

branch

NoYes

Any EV waiting to

resume its charging ?

Record current grid conditions


Run a power flow with the new load conditions


Yes

No



Resume the charging of the first 5% of EV on

the halted EV list

Yes

Yes

Restore the recorded previous grid conditions

No

End of day was reached ?No

List of EV whose charging

was halted is empty ?

Yes


No


Update the list of EV whose charging was

halted (**)


halted

Yes

Increase the

share of EV in

1%

Yes

Next hour

Voltage or

congestion problem ?

Halt the charging

of 5% of the EV

connected in the

problematic node

Voltage Congestion

No

Sm

art C

ha

rgin

g

Define the connection period of each EV (*)

(*) The EV connection period was

defined according to the mobility

statistical data gathered for Portugal,

published in [17].

(**) This list is updated and sorted

each cycle, giving priority to EV who

will disconnect first from the grid.



Results regarding the maximum allowable EV integration

Dumb charging approach – 10% allowable EV integration

Dual tariff policy – 14% allowable EV integration (considering that 25%

of the EV only charge during the cheaper period – valley hours)

Smart charging strategy – 52% allowable EV integration (considering

that 50% of EV owners adhered to the smart charging system)



Scenarios used to evaluate EV impacts in the network 1 power flow for

each hour was performed

Scenario 0 Scenario 1 Scenario 2 Scenario 3 Scenario 4

N.º of Vehicles 12700 12700 12700 12700 12700

EVs % 0% 5% 10% 14% 52%

Hybrid Share - 20% 20% 20% 20%

Medium EV Share - 40% 40% 40% 40%

Large EV Share - 40% 40% 40% 40%

Total Energy consumption (MWh) 277.1 283.2 294.0 301.7 388.1

Dumb

charging

limit

Dual

tariff

limit

Smart

charging

limit

Test

case



EV electricity demand with the dumb charging (52% EV penetration):

was calculated taking into account mobility statistical data for Portugal

When people arrive

home from work

0

5000

10000

15000

20000

25000

30000

35000

1 5 9 13 17 21

Po

wer

dem

and

(kW

)

Time (h)

Dumb Charging

EV load

Household load

Total load

0

5000

10000

15000

20000

25000

30000

35000

1 5 9 13 17 21

Po

wer

dem

and

(kW

)

Time (h)

Dumb Charging

EV load

Household load

Total load



EV electricity demand with the dual tariff policy (52% EV penetration):

was calculated taking into account mobility statistical data for Portugal

was assumed that 25% of EV owners adhered to this scheme, shifting their EV

charging to lower energy price periods

0

1

2

3

4

5

6

7

8

0

5000

10000

15000

20000

25000

30000

35000

1 5 9 13 17 21

Ele

ctr

icit

y p

rice

Po

wer

dem

and

(kW

)

Time (h)

Dual Tariff Policy

EV load

Household load

Total load

Electricity price

0

1

2

3

4

5

6

7

8

0

5000

10000

15000

20000

25000

30000

35000

1 5 9 13 17 21

Ele

ctr

icit

y p

rice

Po

wer

dem

and

(kW

)

Time (h)

Dual Tariff Policy

EV load

Household load

Total load

Electricity price

When electricity is

cheaper



EV electricity demand with the smart charging (52% EV penetration):

was assumed that 50% of EV owners adhered to this scheme, being their

charging controlled by the aggregator

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

1 5 9 13 17 21

Po

wer

dem

and

(kW

)

Time (h)

Smart Charging

EV load

Household load

Total load

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

1 5 9 13 17 21

Po

wer

dem

and

(kW

)

Time (h)

Smart Charging

EV load

Household load

Total load

Avoids peak load

increase



Results Changes in load diagrams with 52% of EV penetration

0

5

10

15

20

25

30

35

1 5 9 13 17 21

Lo

ad

(M

W)

Hour

Without EV

Dumb Charging

Dual Tariff Policy

Smart Charging



Results Voltages obtained for the worst bus during the peak hour

0,82

0,84

0,86

0,88

0,90

0,92

0,94

0,96

0,98

No Evs 5% Evs 10% Evs 14% Evs 52% Evs

Vo

ltag

e (

p.u

.)

No EVs Dumb charging Dual tariff policy Smart charging



Results Worst branch loading obtained during the peak hour

0

20

40

60

80

100

120

140

160

No Evs 5% Evs 10% Evs 14% Evs 52% Evs

Rat

ing

(%)

No EVs

Dumb charging

Dual tariff policy

Smart charging



Results Daily losses

0%

1%

2%

3%

4%

5%

6%

7%

0

5

10

15

20

25

30

Without EV 10% EV 14% EV 52% EV

Lo

sse

s re

lative

va

lue

(%

)

Lo

sse

s (M

Wh

)

Losses with no EV (MWh)

Dumb charging losses (MWh)

Dual tariff policy losses (MWh)

Smart charging losses (MWh)

Losses relative value (% of the energy consumption)

0%

1%

2%

3%

4%

5%

6%

7%

0

5

10

15

20

25

30



Results Branches loading overview (peak hour), with 52% EV penetration

No EV Dumb charging

Dual tariff policy Smart charging


b) Case study B: typical Portuguese LV grid

Objectives:

Develop a smart charging strategy to:

1. Maximize the number of EV that can be safely connected into the

grid (without reinforcing it)

2. Minimize the renewable energy wasted (in scenarios where

renewable generation surplus might exist)



1st Objective – Maximize the number of

EV that can be safely connected into the

grid (without reinforcing it)



Grid architecture:

Residential LV network (400 V)

Feeding point voltage 1 p.u.

Feeder capacity 630 kW

250 households

9.2 MWh/day

550 kW peak load

0

20

40

60

80

100

120

1 3 5 7 9 11 13 15 17 19 21 23

% o

f th

e c

on

sum

pti

on

Hour

Total Household Commercial



EV characterization and modelling:

Initially, 20 EV (~5%) were distributed through the grid proportionally to

the residential load installed at each bus

375 vehicles

Annual mileage 12800 km (35 km/day)

EV assumed charging time 4h

EV fleet considered:

• Large EV 24 kWh 40% of the EV fleet

• Medium EV 12 kWh 40% of the EV fleet

• Plug-in Hybrid EV 6 kWh 20% of the EV fleet



Dumb charging and dual tariff policy methodology (same as in case study A)



Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (dumb charging mode)



Yes

No


End of day was reached ?

No

Yes



Next hour

Increase the share of EV in 1%

Algorithm developed

to quantify the

maximum number of

EV that can be safely

integrated into the

grid with the dumb

charging (without

grid reinforcements)


Distribution Networks –

Preliminary Studies

b) Case study B: typical Portuguese

LV grid

Smart charging methodology

(same as in case study A)

Algorithm developed to

maximize the number of EV

that can be safely integrated

in the grid with the smart

charging (without grid

reinforcements)



Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (as in the dumb

charging mode)



Halt the charging

of 2% of the EV

connected in each

node downstream

the problematic

branch

NoYes

Any EV waiting to

resume its charging ?

Record current grid conditions




Yes

No



Resume the charging of the first 5% of EV on

the halted EV list

Yes

Yes

Restore the recorded previous grid conditions

No

End of day was reached ?No

List of EV whose charging

was halted is empty ?

Yes


No



halted (**)


halted

Yes

Increase the

share of EV in

1%

Yes

Next hour

Voltage or

congestion problem ?

Halt the charging

of 5% of the EV

connected in the

problematic node

Voltage Congestion

No

Sm

art C

ha

rgin

g

Define the connection period of each EV (*)

(*) The EV connection period was

defined according to the mobility

statistical data gathered for Portugal,

published in [17].

(**) This list is updated and sorted

each cycle, giving priority to EV who

will disconnect first from the grid.



Results regarding the maximum allowable EV integration

Dumb charging approach – 11% allowable EV integration

Smart charging strategy – 61% allowable EV integration (considering

that 50% of EV owners adhered to the smart charging system)



Scenarios used to evaluate EV impacts in the network 1 three-phase

power flow for each hour was performed

Scenario 0 Scenario 1 Scenario 1

N.º of Vehicles 375 375 375

EVs % 0% 11% 61%

Hybrid Share - 20% 20%

Medium EV Share - 40% 40%

Large EV Share - 40% 40%

Total Energy consumption (MWh) 9.17 9.81 12.74

Dumb

charging

limit

Smart

charging

limit



Total electricity demand with the dumb and smart charging (61% EV penetration):

The dumb charging curve was calculated taking into account mobility statistical

data for Portugal

The smart charging curve obtained assuming that 50% of EV owners adhered to

this scheme, being their charging controlled by the aggregator

0

200

400

600

800

1000

1 3 5 7 9 11 13 15 17 19 21 23

kW

Hour

Without EVs

Dumb Charging

Smart charging

Feeder capacity



Results Voltages obtained for the worst bus during the peak hour

0,90

0,91

0,92

0,93

0,94

0,95

0,96

0,97

No EVs 11% - Dumb Charging

11% - Smart Charging

61% - Dumb Charging


Vo

ltag

e (

p.u

.)

Phase R Phase S Phase T



Results Worst branch loading obtained during the peak hour

6372

64

124

75

0

20

40

60

80

100

120

140



61% - Dumb Charging


Co

nge

stio

n L

eve

l (%

)



Results Daily losses

17 11

130

83

0

20

40

60

80

100

120

140

Dumb charging

Smart charging

Dumb charging

Smart charging

Incr

eas

e in

lo

sse

s d

ue

to

EV

s co

nsu

mp

tio

n (

%)

11% EVs 61% EVs



Results Load imbalance between phases

4,86,0

4,7

14,2 14,0

0

2

4

6

8

10

12

14

16



61% - Dumb Charging


Load

Im

bal

ance

in

th

e M

V/L

V T

ran

sfo

rme

r (%

)

, , , ,

, ,% 100

R S T R S T

MAX MIN

R S T

AVERAGE

P PLI

P



2nd Objective – Minimize the renewable

energy wasted (in scenarios where

renewable generation surplus exist)



Selected scenario A wet and windy day in 2011

Portuguese situation in 2011:

Around 5 GW of wind power + “must run” of the thermal units renewable

energy might be wasted (in low demand periods)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

1 3 5 7 9 11 13 15 17 19 21 23

P (M

W)

Hour

DER - Hydro Hydro - Run of River Coal

NG Fuel Der - Thermal

Hydro (with reservoir) DER - Wind Demand

Portuguese Generation Profile for a Windy Day in 2011

Installed Capacity (MW)

Hydro - 4957

Thermal - 5820

CHP - 1463

Wind - 5000

Others - 52

Installed Capacity (MW)

Wind energy produced - 51 GWh



Demand change due to 11% of EV Results obtained for the LV grid were

transposed to the complete electric power system

LV Grid Load Diagram Portuguese Generation Profile

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

1 3 5 7 9 11 13 15 17 19 21 23

P (M

W)

Hour

DER - Hydro Hydro - Run of River

Coal NG

Fuel DER - Thermal

Hydro (with reservoir) DER - Wind

Demand without EVs Demand with EVs - Smart charging

Demand with EVs - Dumb charging

Renewable Energy Wasted!

Wind

Energy

Wasted 31

30

15

0 5 10 15 20 25 30 35

No EVs

Dumb Charging

Smart Charging

%

0

200

400

600

800

1000

1 3 5 7 9 11 13 15 17 19 21 23

kW

Hour

Without EVs

Dumb Charging

Smart charging

Feeder capacity



Demand change due to 61% of EV Results obtained for the LV grid were

transposed to the complete electric power system

0

200

400

600

800

1000

1 3 5 7 9 11 13 15 17 19 21 23

kW

Hour

Without EVs

Dumb Charging

Smart charging

Feeder capacity

LV Grid Load Diagram National Generation Profile

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

1 3 5 7 9 11 13 15 17 19 21 23

P (M

W)

Hour

DER - Hydro Hydro - Run of River

Coal NG

Fuel DER - Thermal

Hydro (with reservoir) DER - Wind

Demand without EVs Demand with EVs - Dumb charging

Demand with EVs - Smart charging

Large Peak Load Increase!

Wind Energy Wasted

31

26

1

0 5 10 15 20 25 30 35

No EVs

Dumb Charging

Smart Charging

%



Daily CO2 emissions

29 26

11

3031

36

0

10

20

30

40

50

60

70

Without EVs 11% EVs* 61% EVs*

Dai

ly C

O2

em

issi

on

s (k

ton

)

Power system emissions (including: extraction and processing; raw material

transport; and electricity generation)

Light vehicles emissions (well-to-wheel)

*Smart charging


c) Overall conclusions

Losses increase as the number of EV rises

Overall GHG emissions decrease as the number of EV rises

Voltages and branches loading worsen as the number of EV increases

~10% is the number of EV that can be integrated with the dumb charging

~15% is the number of EV that can be integrated with the dual tariff policy

When comparing with the dumb charging and with the dual tariff policy, the smart

charging allows:

decreasing grid losses and consequently GHG emissions

improving voltage profiles and branches’ congestion levels

safely integrating 50-60% of EV

avoiding the loss of renewable energy

Results are highly dependent on where and when EV will charge A Monte Carlo

simulation method should be used to obtain more accurate results


a) Introduction

The utilization of a Monte Carlo method to perform impact studies is more

adequate allows reducing the uncertainties by running a high number of

different scenarios

This approach allows obtaining average values and confidence intervals for

several system indexes, like buses voltages, branches loading and energy

losses


b) Case study: Flores Island network (Azores Archipelago)

Grid architecture:

Isolated MV network

(15 kV)

Typical winter day

consumption 47.55

MWh

2.59 MW peak load

(occurs at 19:30 h)

Average power factor

0.77

Island light vehicles

fleet 2285 vehicles

2 scenarios studied

25% and 50% EV

penetration

1

2 7 8 17 41

3 9 42

4 19 43

5 11 20 29 31 44

6 12 21 32 37

13 22 33 38

14 23 34 39

15 24 40

16 25

26

27

Swing Bus

Thermal Power Plant Hydro Power Plant

Wind Farm

28

18

35

4530

24 Bus

Load

Power Plant

Line

10

36



EV movement along one day was simulated using a discrete-time non-Markovian

process to define the states of all the EV at each 30 minutes interval (48 time instants)

In each time instant, EV can be in four different states: in movement, parked in

industrial area, parked in commercial area, parked in residential area

The EV state for each time instant is defined according to the probabilities specified for

that time instants and according to the discrete-time non-Markovian process

In Movement

Parked in

Industrial Area

Parked in

Residential Area

Parked in

Commercial Area

𝒕 = 𝒏

𝑃𝐶→𝑀𝑡=𝑛

𝑃𝐶𝑡=𝑛

In Movement

Parked in

Industrial Area

Parked in

Residential Area

Parked in

Commercial Area

𝒕 = 𝒊

𝑃𝐶→𝑀𝑡=𝑖

𝑃𝐶𝑡=𝑖

In Movement

Parked in

Industrial Area

Parked in

Residential Area

Parked in

Commercial Area

𝑃𝑀𝑡=1

𝑃𝑀→𝑅𝑡=1

𝑃𝑅→𝑀𝑡=1

𝑃𝑀→𝐼𝑡=1 𝑃𝐼→𝑀

𝑡=1

𝑃𝑀→𝐶𝑡=1

𝑃𝐶→𝑀𝑡=1

𝑃𝑅𝑡=1 𝑃𝐼

𝑡=1 𝑃𝐶𝑡=1

𝒕 = 𝟏

𝑃𝐼𝑡=𝑛 𝑃𝑅

𝑡=𝑛

𝑃𝐼𝑡=𝑖 𝑃𝑅

𝑡=𝑖



The state transition probabilities applied were determined by analyzing the common traffic

patterns of Portuguese drivers

It was gathered information about the number of car journeys made per each 30 minutes

interval, along a typical weekday, as well as the journey purpose and its average duration

With this data, it was possible to define the probabilities of an EV reside in a given state at a

given time instant



Define EV location for parked EV:

all bus loads were classified as industrial, commercial or residential

the probability of an EV be located at a specific bus was calculated with the

following equations:

𝑃𝐵𝑢𝑠 𝑘𝑅 =

𝐿𝑜𝑎𝑑𝐵𝑢𝑠 𝑘𝑅

𝐿𝑜𝑎𝑑𝑅 𝑃𝐵𝑢𝑠 𝑘𝐼 =

𝐿𝑜𝑎𝑑𝐵𝑢𝑠 𝑘𝐼

𝐿𝑜𝑎𝑑𝐼 𝑃𝐵𝑢𝑠 𝑘𝐶 =

𝐿𝑜𝑎𝑑𝐵𝑢𝑠 𝑘𝐶

𝐿𝑜𝑎𝑑𝐶


Distribution Networks – A Monte

Carlo Method

d) Monte Carlo algorithm

1. Make the initial characterization of all the EV:

• initial state

• the bus they are initially located

• battery capacity (kWh)

• slow charging rated power (kW)

• initial SOC (%)

• energy consumption (kWh/km)

• owners’ behaviour

Ind

exe

s

up

da

te

Sa

mp

le g

en

era

tion

an

d e

va

lua

tion

Define EV initial conditions (initial state, bus, battery capacity, slow charging rated

power, initial SOC, energy consumption and driver behaviour)

Draw EV states and the buses where “parked” EV are located, for the next time

instant

Determine the new load at each bus

Power flow analysis

End of the day was reached ?

Monte Carlo finishing criteria was met ?

No

Compile results: power demand, voltages, branches loading, energy losses, peak

power, number of voltage and branches ratings violations

Yes

Yes

EV battery SOC < 30% ?

Yes

Update EV batteries SOC

Update of grid technical indexes and vehicle usage indicators in a hourly and daily

basis

Yes

No

What is the EV driver behaviour ?

EV is parked in

residential area ?

Yes

EV is parked in

residential area ?

EV arrived home from the

last journey of the day ?

Yes

EV starts charging

EV do not charge

No

No No

EV charge

only when

it needs EV charge

whenever

possible

EV charge at the

end of the day or

whenever is

convenient and the

driver has time

No

GAUSSIAN DISTRIBUTIONS FOR INITIAL EV CHARACTERIZATION

Average Standard

deviation

Maximum

value

allowed

Minimum

value allowed

Battery capacity (kWh) 24.73 17.19 85.00 5.00

Slow charging rated power

(kW) 3.54 1.48 10.00 2.00

Energy consumption

(kWh/km) 0.18 0.12 0.85 0.09

Initial battery SOC (%) 50.00 25.00 85.00 15.00

DRIVERS’ BEHAVIOURS CONSIDERED

Percentage of the

responses

EV charge at the end of the day 33%

EV charge only when it needs 23%

EV charge whenever possible 20%

EV charge whenever is convenient and the driver has time 24%

30% SOC



2. Samples generation:

• Simulate EV movement along one typical weekday define EV states

• Attribute a bus location to parked EV

• Update battery SOC for EV in movement:

o if an EV was in movement in time instant t and its battery SOC went below a

predefined threshold (assumed to be 15%) in time instant t+1, it was considered that

the EV would make a short detour to a fast charging station for recharging purposes

o the fast charging was assumed to be made during 15 minutes with a power of 40 kW

o the fast charging station was considered to be installed in bus 12, as this is located

near one of the more populated areas of the island, with a high number of potential

clients

• Compute the total amount of power required from the network, discriminated per bus and

per time instant

GAUSSIAN DISTRIBUTIONS FOR EV MOVEMENT CHARACTERIZATION

Average Standard

deviation

Maximum

value

allowed

Minimum

value allowed

Travelled distance in

common journeys (km) 9.01 4.51 27.03 0.90

Travelled distance to fast

charging station (km) 4.51 2.25 13.52 0.45



3. Samples evaluation:

• Made by running a power flow for each time instant and by gathering information about:

o Voltage profiles

o Power flows in the lines

o Energy losses

o Highest peak load

4. Terminating the Monte Carlo process 2 criteria used:

• Number of iterations 10000

• Variation in the last 10 iterations of the aggregated network load variances (of each one of

the 48 time instants) < 1𝑒−4

∆𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 = 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒𝑗𝑖 − 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒𝑗−10

𝑖 < 1𝑒−4


e) Results

Power demand:


e) Results

Voltage profile of one feeder (buses 17 to 27):


e) Results

Network voltage profiles for the highest peak load identified:


e) Results

Voltage lower limit violation probability:

𝑃𝑉.𝑙𝑜𝑤𝑒𝑟 𝑙𝑖𝑚𝑖𝑡 𝑣𝑖𝑜𝑙𝑎𝑡𝑖𝑜𝑛𝐵𝑢𝑠 𝑘 =

𝑉. 𝑙𝑜𝑤𝑒𝑟 𝑙𝑖𝑚𝑖𝑡 𝑣𝑖𝑜𝑙𝑎𝑡𝑖𝑜𝑛𝑠𝐵𝑢𝑠 𝑘

𝑁𝑟. 𝑖𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠 × 48× 100


e) Results

Branches loading:

No EV

25% EV

50% EV


e) Results

Average daily energy losses:


e) Results

Evolution of the network load variances with the highest variation rate:


e) Results

Network load variances of the 48 time instants:


f) Conclusions

The simulation platform developed proved to be very efficient in performing a realistic

evaluation of the impacts that result from a massive integration of EV in distribution networks

Allows:

evaluating the steady state operating conditions of the grid

identifying the most critical operation scenarios and the network components that are

subjected to more demanding conditions and that might need to be upgraded

The island network is very robust is capable of integrating a large number of EV without

the occurrence of lines overloading and voltage limits violations (~25%)

With 50% of EV a large number of voltage violations were registered efficient

mechanisms to manage EV charging (smart charging) are required to avoid making large

investments in network reinforcements

For large EV integration scenarios, losses will become a very important issue for system

operator their value grows:

58% from the scenario without EV to the one with 25% of EV

140% from the scenario without EV to the one with 50% of EV

Energy losses might be greatly reduced by using an EV smart charging strategy

5. Final Remarks

EV integration in interconnected systems:

Due to the reduced energy consumption and capability of providing

services to the grid, it is impossible to EV participate in the markets

individually EV suppliers/aggregators must exist for this purpose

Even under the EV supplier/aggregator management, EV might still create

several problems in distribution networks A grid monitoring mechanism

must exist (independent from the aggregator and headed by the DSO), with

the capability of manage EV charging, in order to avoid those problems

EV integration in small isolated systems:

As usually these systems do not have an electricity market, EV

suppliers/aggregators are not needed Only the grid monitoring mechanism

controlled by the DSO must exist

5. Final Remarks

EV integration limitations:

without any control actions over EV charging (dumb charging), it is

impossible to integrate a large number of EV in common electricity networks

network reinforcements are required

if EV charging is controlled (smart charging), even in accordance with their

owners requirements, a larger number of EV might be integrated without

investments in grid reinforcements

nonetheless, if the number of EV keeps growing, there will be a moment in

time where reinforcement will be inevitable, even when the smart charging is

applied…

5. Final Remarks

Network impacts As the number of EV rises:

losses increase

overall GHG emissions decrease

voltages and branches loading worsen

Smart charging vs. Dumb charging:

decrease grid losses and, consequently, GHG emissions

improve voltage profiles and branches loading

allow the integration of a higher number of EV without reinforcements

allow an effective exploitation of renewable generation surplus (when such

problem exists)

F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen,

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